4,684 research outputs found
GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing
Camera-based tactile sensing is a low-cost, popular approach to obtain highly
detailed contact geometry information. However, most existing camera-based
tactile sensors are fingertip sensors, and longer fingers often require
extraneous elements to obtain an extended sensing area similar to the full
length of a human finger. Moreover, existing methods to estimate proprioceptive
information such as total forces and torques applied on the finger from
camera-based tactile sensors are not effective when the contact geometry is
complex. We introduce GelSight Svelte, a curved, human finger-sized,
single-camera tactile sensor that is capable of both tactile and proprioceptive
sensing over a large area. GelSight Svelte uses curved mirrors to achieve the
desired shape and sensing coverage. Proprioceptive information, such as the
total bending and twisting torques applied on the finger, is reflected as
deformations on the flexible backbone of GelSight Svelte, which are also
captured by the camera. We train a convolutional neural network to estimate the
bending and twisting torques from the captured images. We conduct gel
deformation experiments at various locations of the finger to evaluate the
tactile sensing capability and proprioceptive sensing accuracy. To demonstrate
the capability and potential uses of GelSight Svelte, we conduct an object
holding task with three different grasping modes that utilize different areas
of the finger. More information is available on our website:
https://gelsight-svelte.alanz.infoComment: Submitted and accepted to 2023 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2023
Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale
Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation
Intelligent interaction with the physical world requires perceptual abilities
beyond vision and hearing; vibrant tactile sensing is essential for autonomous
robots to dexterously manipulate unfamiliar objects or safely contact humans.
Therefore, robotic manipulators need high-resolution touch sensors that are
compact, robust, inexpensive, and efficient. The soft vision-based haptic
sensor presented herein is a miniaturized and optimized version of the
previously published sensor Insight. Minsight has the size and shape of a human
fingertip and uses machine learning methods to output high-resolution maps of
3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing
performance, with a mean absolute force error of 0.07 N and contact location
error of 0.6 mm across its surface area. Minsight's utility is shown in two
robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables
the robot to track the movements of a human finger based only on tactile data.
Second, the informative value of the sensor output is shown by detecting
whether a hard lump is embedded within a soft elastomer with an accuracy of
98%. These findings indicate that Minsight can give robots the detailed
fingertip touch sensing needed for dexterous manipulation and physical
human-robot interaction
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